Secure Operation Boundary Building Technology Based on Machine Learning

The conditions for the safe and stable operation of a large power grid are highly interdependent and difficult to predict. In order to accurately understand the operational state of a large power grid, an efficient assessment model for its safe operation is particularly important. In this paper, the machine learning model is combined with the power system safety operation boundary model, and the efficient data processing ability of the machine learning model is used to construct a large power grid safety operation evaluation model based on the residual network to achieve accurate prediction of the operation status of the power system. Based on the evaluation model, an active learning strategy based on the sample training set is proposed, which improves the training effect of the evaluation model. Combined with the safety evaluation model, a safety operation boundary construction method based on residual network is obtained by support vector machine algorithm, and a safety margin estimation model of system operation points is constructed based on this method, which realizes the quantification of the operation point of the power system. Finally, the IEEE9 node system is simulated to verify the effectiveness of the proposed method and improve the mastery of the power system.